DEG-analysis

workflowr

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Last updated: 2024-01-17

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Knit directory: UPF1-FMR1/

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---

Introduction

source(here::here("code/libraries.R"))
library(stargazer)
library(ggfortify)
library(glue)
library(cowplot)
library(broom)
library(glmpca)
library(naniar)
library(gridExtra)
library(EnsDb.Hsapiens.v86)
library(ggrepel)
library(org.Hs.eg.db)
library(msigdbr)
library(fgsea)

goSummaries <- url("https://uofabioinformaticshub.github.io/summaries2GO/data/goSummaries.RDS") %>%
    readRDS() %>%
    mutate(ontology = as.character(ontology))

getGeneLists <- function(pwf, goterms, genome, ids){
    gene2cat <- getgo(rownames(pwf), genome, ids)
    cat2gene <- split(rep(names(gene2cat), sapply(gene2cat, length)),
                      unlist(gene2cat, use.names = FALSE))
    out <- list()
    for(term in goterms){
        tmp <- pwf[cat2gene[[term]],]
        tmp <- rownames(tmp[tmp$DEgenes > 0, ])
        out[[term]] <- tmp
    }
    out


}

txdf = transcripts(EnsDb.Hsapiens.v86, return.type="DataFrame")
tx2gene = as.data.frame(txdf[,c("tx_id","gene_id", "tx_biotype")])

ah <- AnnotationHub() %>%
    subset(species == "Homo sapiens") %>%
    subset(rdataclass == "EnsDb") %>% 
  subset(genome == "GRCh38")

ensDb <- ah[["AH109606"]]
grTrans <- transcripts(ensDb)
trLengths <- exonsBy(ensDb, "tx") %>%
    width() %>%
    vapply(sum, integer(1))
mcols(grTrans)$length <- trLengths[names(grTrans)]

genesGR = genes(ensDb)
transGR = transcripts(ensDb)

mcols(transGR) = mcols(transGR) %>%
  cbind(
    transcriptLengths(ensDb)[rownames(.), c("nexon", "tx_len")]
  )


id2Name <- structure(
  genesGR$gene_name,
  names = genesGR$gene_id
) %>% 
  .[!duplicated(names(.))]

salmon.files = ("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon")
salmon = list.files(salmon.files, pattern = "transcripts$", full.names = TRUE)
all_files = file.path(salmon, "quant.sf")
sample_names = gsub("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon/", "", salmon)
sample_names = gsub(".gz_transcripts", "", sample_names)
sample_names = gsub("\\_.*", "", sample_names)
names(all_files) <- sample_names
md = read.csv(here::here("data/Sample_info.csv"), header= TRUE) %>%  
  #mutate(files = file.path(salmon, "quant.sf")) %>% 
  dplyr::rename("names" = "GeneWiz.ID", 
                "Group" = "Sample.type") %>% 
  mutate(Group = ifelse(Group == "MC" | Group == "FC", "Control", Group)) %>%
  dplyr::select(names,everything()) %>% 
  mutate(names = gsub("\\_.*", "", names) )
md = md[order(match(md$names, sample_names)),]
md %<>% 
  rownames_to_column("random") %>% 
#  column_to_rownames("names") %>%
  dplyr::select(-random) %>%
  mutate(files = all_files)

md %<>% dplyr::filter(Group != "FMR1") %>%
 dplyr::filter(names != "23-LDJ6767") %>%
dplyr::filter(names != "202")
all_files = all_files[names(all_files) %in% md$names]

txi_genes =  tximport(all_files, type="salmon", txOut=FALSE,
                      countsFromAbundance="scaledTPM", tx2gene = tx2gene, ignoreTxVersion = TRUE, ignoreAfterBar = TRUE)
keep.genes = (rowSums(txi_genes$abundance >= 1 ) >= 3)

Differential gene expression analysis

txi_genes_filtered = txi_genes$counts[keep.genes,]
y <- DGEList(txi_genes_filtered)


design <- model.matrix(~Batch +Group + Sex, data = md) %>% 
    set_colnames(gsub(pattern = "Group", replacement ="", x = colnames(.)))

y <- calcNormFactors(y)
v <- voom(y, design)

fit = lmFit(v, design) %>% 
    eBayes()

summary(decideTests(fit, lfc =0))

       (Intercept) Batch  FRAX  UPF1  SexM
Down           398   439   533   233    11
NotSig        2189 12093 11814 12551 13189
Up           10627   682   867   430    14

UPF1 DEGs

upf1_results_lfc = topTable(fit,coef = "UPF1", number = Inf) %>% 
                     mutate(res = ifelse(logFC > 0& adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig"))) %>% 
  rownames_to_column("ensembl_gene_id") %>%
   mutate(SYMBOL = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="SYMBOL",keytype="ENSEMBL", multiVals="first")) %>%
  mutate(res = ifelse(logFC > 0 & adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0  & adj.P.Val < 0.05, "Downregulated", "NotSig")))

DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026",  "NotSig" = "#E5E5E5")


volc_upf1 = upf1_results_lfc  %>%
    ggplot(aes(y = -log10(adj.P.Val), 
               x =  logFC , 
               colour = res,
               size =-log10(adj.P.Val), 
               label= SYMBOL)) +
  geom_point(alpha = 0.8) +
  # geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
  #  geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
    scale_colour_manual(values = DEColours) + theme_classic() + 
    theme(axis.title.y = element_text(size = 12)) +
    geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
    labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
  xlim(-8.5, 8.5) + ylim(0, 7.5)


# volc = volc_upf1+ geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
#                       "SMG5", "SMG6",
#                       "UPF3A", "ATF4", 
#                       "GADD5G")),
#             aes(label=SYMBOL),   position=position_dodge(width = 0.9), 
#              vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in UPF1 relative to controls using limma/voom")
# 
# 
# volc 

my_gg = volc_upf1 + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

Volcano plot showing distribution of differentially expressed genes using the limma/voom pipeline

upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  dplyr::arrange(adj.P.Val) %>% 
  DT::datatable(caption="DEGS in UPF1 relative to controls using limma/voom pipeline") 

FRAX DEGs

frax_results_lfc = topTable(fit,coef = "FRAX", number = Inf) %>% 
                     mutate(res = ifelse(logFC > 0  & adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig"))) %>% 
  rownames_to_column("ensembl_gene_id") %>%
   mutate(SYMBOL = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="SYMBOL",keytype="ENSEMBL", multiVals="first")) %>% 
    mutate(res = ifelse(logFC > 0 & adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0  & adj.P.Val < 0.05, "Downregulated", "NotSig"))) 

DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026",  "NotSig" = "#E5E5E5")


volc_frax = frax_results_lfc  %>%
    ggplot(aes(y = -log10(adj.P.Val), 
               x =  logFC , 
               colour = res,
               size =-log10(adj.P.Val), 
               label= SYMBOL)) +
  geom_point(alpha = 0.8) +
  # geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
  #  geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
    scale_colour_manual(values = DEColours) + theme_classic() + 
    theme(axis.title.y = element_text(size = 12)) +
    geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
    labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
  xlim(-8.5, 8.5) + ylim(0, 7.5)


# volc = volc + geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
#                       "SMG5", "SMG6",
#                       "UPF3A", "ATF4", 
#                       "GADD5G", "FMR1")),
#             aes(label=SYMBOL),   position=position_dodge(width = 0.9), 
#              vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in FRAX relative to controls using limma/voom pipeline")


my_gg =  volc_frax + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

Overlap of UPF1 and FRAX

• Number of genes overlapping

library(VennDiagram)
x= list("UPF1 DEGs" = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05], 
        "FRAX DEGs" = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05])

ggvenn(x)

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

overlaps = calculate.overlap(x)
upf1_only = overlaps$a1[!overlaps$a1 %in% overlaps$a3]
fmr1_only = overlaps$a2[!overlaps$a2 %in% overlaps$a3]

upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(frax_results_lfc %>% 
               dplyr::filter(adj.P.Val < 0.05) %>%
                dplyr::select(ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>% 
  ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 222 overlapping genes")

Overlap of significant genes from FRAX and UPF1

Past versions of unnamed-chunk-15-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

• Gene ontology

genes = as.integer(upf1_results_lfc$ensembl_gene_id %in% overlaps$a3)
not_na = !is.na(genes)
names(genes) = upf1_results_lfc$ensembl_gene_id
genes = genes[not_na]
    
pwf=nullp(genes,plot.fit = FALSE, 'hg19','ensGene')
Go.wall=goseq(pwf, 'hg19','ensGene', use_genes_without_cat = TRUE)
goterms <- Go.wall$category
goList <- getGeneLists(pwf, goterms, 'hg19','ensGene')
Go.wall$EnsemblID <- sapply(Go.wall$category, function(x) goList[[x]])
Go.wall$EnsemblID <- vapply(Go.wall$EnsemblID, paste, collapse = ", ", character(1L))

goRes = Go.wall %>%
            dplyr::select(-under_represented_pvalue) %>%
            dplyr::filter(numDEInCat > 0) %>%
            dplyr::rename(id = category) %>%
            left_join(goSummaries)
nGenes <- c(BG = sum(genes == 0), DE = sum(genes))
goFinal <- goRes %>%
  dplyr::filter(numDEInCat > 2) %>% 
  dplyr::filter(shortest_path > 3) %>%
  arrange(desc(shortest_path), EnsemblID) %>% 
  distinct(EnsemblID, .keep_all = TRUE) %>%
  arrange(over_represented_pvalue) %>% 
  mutate(Expected = round(nGenes[["DE"]] * numInCat / nGenes[["BG"]], 0)) %>%
            mutate(adjP = p.adjust(over_represented_pvalue, "bonferroni"),
                   FDR = p.adjust(over_represented_pvalue, "fdr"),
                   Sig_adjP = adjP < 0.05, Sig_FDR = FDR < 0.05) %>%
            dplyr::filter(Sig_adjP, numDEInCat > Expected) %>%
  dplyr::select(term, everything()) 
  
  
goFinal %>% 
  DT::datatable(caption="222 overlapping genes")

• UPF1 specific genes

upf1_results_lfc %>% 
  dplyr::filter(ensembl_gene_id %in% upf1_only) %>% 
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(frax_results_lfc %>% 
                dplyr::select(ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>%
  ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 441 UPF1 specific genes")

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

genes = as.integer(upf1_results_lfc$ensembl_gene_id %in% upf1_only)
not_na = !is.na(genes)
names(genes) = upf1_results_lfc$ensembl_gene_id
genes = genes[not_na]
    
pwf=nullp(genes,plot.fit = FALSE, 'hg19','ensGene')
Go.wall=goseq(pwf, 'hg19','ensGene', use_genes_without_cat = TRUE)
goterms <- Go.wall$category
goList <- getGeneLists(pwf, goterms, 'hg19','ensGene')
Go.wall$EnsemblID <- sapply(Go.wall$category, function(x) goList[[x]])
Go.wall$EnsemblID <- vapply(Go.wall$EnsemblID, paste, collapse = ", ", character(1L))

goRes = Go.wall %>%
            dplyr::select(-under_represented_pvalue) %>%
            dplyr::filter(numDEInCat > 0) %>%
            dplyr::rename(id = category) %>%
            left_join(goSummaries)
nGenes <- c(BG = sum(genes == 0), DE = sum(genes))
goFinal <- goRes %>%
  dplyr::filter(numDEInCat > 2) %>% 
  dplyr::filter(shortest_path > 4) %>%
  arrange(desc(shortest_path), EnsemblID) %>% 
  distinct(EnsemblID, .keep_all = TRUE) %>%
  arrange(over_represented_pvalue) %>% 
  mutate(Expected = round(nGenes[["DE"]] * numInCat / nGenes[["BG"]], 0)) %>%
            mutate(adjP = p.adjust(over_represented_pvalue, "bonferroni"),
                   FDR = p.adjust(over_represented_pvalue, "fdr"),
                   Sig_adjP = adjP < 0.05, Sig_FDR = FDR < 0.05) %>%
            dplyr::filter(Sig_adjP, numDEInCat > Expected) %>%
  dplyr::select(term, everything()) 
  
  
goFinal %>% 
  DT::datatable(caption="UPF1 specific genes")

• FMR1 specific genes

frax_results_lfc %>% 
  dplyr::filter(ensembl_gene_id %in% fmr1_only) %>% 
  dplyr::select(SYMBOL,ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val) %>% 
  inner_join(upf1_results_lfc %>% 
                dplyr::select(ensembl_gene_id, logFC, 
                            adj.P.Val), by = "ensembl_gene_id") %>%
  ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 1178 FRAX specific genes")

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

Gene ontology

genes = as.integer(frax_results_lfc$ensembl_gene_id %in% fmr1_only)
not_na = !is.na(genes)
names(genes) = frax_results_lfc$ensembl_gene_id
genes = genes[not_na]
    
pwf=nullp(genes,plot.fit = FALSE, 'hg19','ensGene')
Go.wall=goseq(pwf, 'hg19','ensGene', use_genes_without_cat = TRUE)
goterms <- Go.wall$category
goList <- getGeneLists(pwf, goterms, 'hg19','ensGene')
Go.wall$EnsemblID <- sapply(Go.wall$category, function(x) goList[[x]])
Go.wall$EnsemblID <- vapply(Go.wall$EnsemblID, paste, collapse = ", ", character(1L))

goRes = Go.wall %>%
            dplyr::select(-under_represented_pvalue) %>%
            dplyr::filter(numDEInCat > 0) %>%
            dplyr::rename(id = category) %>%
            left_join(goSummaries)
nGenes <- c(BG = sum(genes == 0), DE = sum(genes))
goFinal <- goRes %>%
  dplyr::filter(numDEInCat > 2) %>% 
  dplyr::filter(shortest_path > 4) %>%
  arrange(desc(shortest_path), EnsemblID) %>% 
  distinct(EnsemblID, .keep_all = TRUE) %>%
  arrange(over_represented_pvalue) %>% 
  mutate(Expected = round(nGenes[["DE"]] * numInCat / nGenes[["BG"]], 0)) %>%
            mutate(adjP = p.adjust(over_represented_pvalue, "bonferroni"),
                   FDR = p.adjust(over_represented_pvalue, "fdr"),
                   Sig_adjP = adjP < 0.05, Sig_FDR = FDR < 0.05) %>%
            dplyr::filter(Sig_adjP, numDEInCat > Expected) %>%
  dplyr::select(term, everything()) 
  
  
goFinal %>% 
  DT::datatable(caption="FRAX specific genes")

Comparison of DEGs with DSB genes from Chakraborty et al

dsbs = read_xlsx(here::here("data/lists/DSBs/FX_DSB_genes.xlsx"), skip = 1)
exp_cl = read_xlsx(here::here("data/lists/DSBs/msrd_results_LCL_Silent_Genes.xlsx"), sheet = 1)

dsbs_lcls = dsbs[!dsbs$ensembl_gene_id %in% exp_cl$ensembl_id,]

• Jozef’s questions

• Is there an overlap of genes with DSBs in FRAXA/FMR1 LCLs and the DEGs in FRAXA/FMR1 LCLs even if across 2x different studies? If not overlap based on p value, is there a trend for these DSB-associated genes to be downregulated? *Is there an overlap of genes with DSBs in FRAXA/FMR1 LCLs and DEGs in UPF3B patient LCLs, the reason being that UPF3B patients have no or very little FMRP left as Saba determined? Also, if no overlap strictly on p value, is there a trend of the DSB genes to be down-regulated in UPF3B LCLs?

• The same as above, but comparing the DSB genes to UPF1 DEGs, etc.

• From this study - using the FRAX DEGs and UPF1 DEGs

x= list("UPF1 DEGs" = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05], 
        "FRAX DEGs" = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05], 
        "Double stranded breaks" = dsbs$ensembl_gene_id)

ggvenn(x)

Past versions of unnamed-chunk-25-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

frax_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  mutate(condition="FRAX") %>% 
  inner_join(dsbs, by = "ensembl_gene_id") %>% 
  rbind(upf1_results_lfc %>% 
          dplyr::filter(adj.P.Val < 0.05) %>%
          mutate(condition="UPF1") %>% 
  inner_join(dsbs, by = "ensembl_gene_id")) %>%
  ggplot(aes(x=condition, fill = res), color = "white") + geom_bar(stat = "count") + 
  theme_bw() + scale_fill_manual(values =c("Upregulated"=  "#720026", 
                                           "Downregulated"= "#2e294e")) + 
  ggtitle("Number of DEGs with DSBs")

Genes with DSBs identified in significantly expressed DEGs

Past versions of unnamed-chunk-26-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

library(ggstatsplot)
frax_results_lfc %>% 
  mutate(res = ifelse(logFC > 0, "Upregulated", 
                      ifelse(logFC < 0, "Downregulated", "NotSig"))) %>%
  mutate(condition="FRAX") %>% 
  inner_join(dsbs, by = "ensembl_gene_id") %>% 
  rbind(upf1_results_lfc %>% 
         mutate(res = ifelse(logFC > 0, "Upregulated", 
                      ifelse(logFC < 0, "Downregulated", "NotSig"))) %>%
          mutate(condition="UPF1") %>% 
  inner_join(dsbs, by = "ensembl_gene_id")) %>%
  ggbarstats(
  x                = res,
  y                = condition,

xlab = "",
legend.title     = "",
  ggplot.component = list(ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge = 2))),
) + scale_fill_manual(values =c("Upregulated"=  "#720026", 
                                           "Downregulated"= "#2e294e"))

Genes with DSBs identified in significantly expressed DEGs

Past versions of unnamed-chunk-27-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

 # ggplot(aes(x=condition, fill = res), color = "white") + geom_bar(stat = "count") + 
  #theme_bw() + scale_fill_manual(values =c("Upregulated"=  "#720026", 
             #                              "Downregulated"= "#2e294e")) + 
  #ggtitle("Number of DEGs with DSBs")

library(GeneOverlap)
enrichment_gene_list_pvalue = list()
enrichment_gene_list_OR = list()

test.list = list()
test.list$BG = frax_results_lfc$ensembl_gene_id
test.list$frax_sig = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05]
test.list$frax_sig_up = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05 & frax_results_lfc$logFC > 0]
test.list$frax_sig_down = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05 & frax_results_lfc$logFC < 0]
test.list$upf1_sig = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05]
test.list$upf1_sig_up = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05 & upf1_results_lfc$logFC > 0]
test.list$upf1_sig_down = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05 & upf1_results_lfc$logFC < 0]
test.list$dsbs = dsbs$ensembl_gene_id
## Maybe an exact test with odds ratio 

for (list in c("frax_sig","frax_sig_up","frax_sig_down","upf1_sig",
               "upf1_sig_up", "upf1_sig_down")) {
    go.obj <- newGeneOverlap(test.list[[list]],
                         test.list$dsbs,
                         genome.size=length(test.list$BG))
    go.obj <- testGeneOverlap(go.obj)
    enrichment_gene_list_pvalue[[paste0(list)]] = go.obj@pval
    enrichment_gene_list_OR[[paste0(list)]] = go.obj@odds.ratio
    
  }


enrichment_GL_pval = do.call(rbind, enrichment_gene_list_pvalue) %>% as.data.frame()
colnames(enrichment_GL_pval)[1] = c("pvalue")
enrichment_GL_pval$padj = p.adjust(enrichment_GL_pval$pvalue, method = "BH")
enrichment_GL_OR  = do.call(rbind, enrichment_gene_list_OR) %>% as.data.frame()
colnames(enrichment_GL_OR)[1] = c("OR")
GL_enrichment = cbind(enrichment_GL_pval, enrichment_GL_OR)

GL_enrichment %>% 
  rownames_to_column("DEGs") %>%
  ggplot(aes(x=OR, y = DEGs)) + geom_bar(stat = "identity", fill = "#edafb8") +
  theme_bw() + geom_vline(xintercept = 1) + 
   theme(axis.text.x = element_text(size = 12, family = "serif", color = "black"),
            axis.text.y = element_text(size = 12, family = "serif", color = "black"),
        axis.title = element_text(size=12, family = "serif"), 
         legend.box.background = element_rect(color = "black"),
          legend.text = element_text(family = "serif"), 
          legend.title = element_text( family = "serif"),
           plot.title = element_text(family = "serif", size =20),         
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line( size=.1 ),
              legend.position = "top",
        strip.text.y = element_text(
        size = 10, face = "bold.italic", family = "serif"
        ),
         strip.text.x = element_text(
        size = 10, family = "serif"
        )) + labs(x="Odds ratio") + ggtitle("Enrichment of DSBs in UPF1 or FRAX")

Past versions of unnamed-chunk-29-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

List of genes with DSBs

frax_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  mutate(condition="FRAX") %>% 
  inner_join(dsbs, by = "ensembl_gene_id") %>%
  dplyr::select(gene = SYMBOL, Expression = res, everything()) %>%
  DT::datatable(caption = "Genes in FRAX DEGs with DSBs")

upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  mutate(condition="FRAX") %>% 
  inner_join(dsbs, by = "ensembl_gene_id") %>%
  dplyr::select(gene = SYMBOL, Expression = res, everything()) %>%
  DT::datatable(caption = "Genes in UPF1 DEGs with DSBs")

Data export

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Australia/Adelaide
tzcode source: system (glibc)

attached base packages:
 [1] grid      stats4    tools     stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] GeneOverlap_1.38.0          ggstatsplot_0.12.1         
 [3] VennDiagram_1.7.3           futile.logger_1.4.3        
 [5] fgsea_1.28.0                msigdbr_7.5.1              
 [7] org.Hs.eg.db_3.18.0         ggrepel_0.9.4              
 [9] EnsDb.Hsapiens.v86_2.99.0   gridExtra_2.3              
[11] naniar_1.0.0                glmpca_0.2.0               
[13] broom_1.0.5                 cowplot_1.1.1              
[15] glue_1.6.2                  ggfortify_0.4.16           
[17] stargazer_5.2.3             ngsReports_2.4.0           
[19] patchwork_1.1.3             AnnotationHub_3.10.0       
[21] BiocFileCache_2.10.1        dbplyr_2.4.0               
[23] openxlsx_4.2.5.2            ggiraph_0.8.8              
[25] DT_0.31                     msigdb_1.10.0              
[27] GSEABase_1.64.0             graph_1.80.0               
[29] annotate_1.80.0             XML_3.99-0.16              
[31] pheatmap_1.0.12             ggvenn_0.1.10              
[33] MetBrewer_0.2.0             ggpubr_0.6.0               
[35] venn_1.11                   viridis_0.6.4              
[37] viridisLite_0.4.2           tximeta_1.20.1             
[39] tximport_1.30.0             goseq_1.54.0               
[41] geneLenDataBase_1.38.0      BiasedUrn_2.0.11           
[43] org.Mm.eg.db_3.18.0         EnsDb.Mmusculus.v79_2.99.0 
[45] ensembldb_2.26.0            AnnotationFilter_1.26.0    
[47] GenomicFeatures_1.54.1      AnnotationDbi_1.64.1       
[49] biomaRt_2.58.0              edgeR_4.0.3                
[51] limma_3.58.1                DESeq2_1.42.0              
[53] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[55] MatrixGenerics_1.14.0       matrixStats_1.2.0          
[57] GenomicRanges_1.54.1        GenomeInfoDb_1.38.2        
[59] IRanges_2.36.0              S4Vectors_0.40.2           
[61] BiocGenerics_0.48.1         corrplot_0.92              
[63] lubridate_1.9.3             forcats_1.0.0              
[65] purrr_1.0.2                 readr_2.1.4                
[67] tidyverse_2.0.0             stringr_1.5.1              
[69] tidyr_1.3.0                 scales_1.3.0               
[71] data.table_1.14.10          readxl_1.4.3               
[73] tibble_3.2.1                magrittr_2.0.3             
[75] reshape2_1.4.4              ggplot2_3.4.4              
[77] dplyr_1.1.4                 workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.3                      ProtGenerics_1.34.0          
  [3] bitops_1.0-7                  insight_0.19.7               
  [5] httr_1.4.7                    RColorBrewer_1.1-3           
  [7] backports_1.4.1               utf8_1.2.4                   
  [9] R6_2.5.1                      statsExpressions_1.5.2       
 [11] lazyeval_0.2.2                mgcv_1.9-0                   
 [13] withr_2.5.2                   prettyunits_1.2.0            
 [15] cli_3.6.2                     formatR_1.14                 
 [17] sandwich_3.1-0                labeling_0.4.3               
 [19] sass_0.4.8                    mvtnorm_1.2-4                
 [21] pbapply_1.7-2                 Rsamtools_2.18.0             
 [23] systemfonts_1.0.5             rstudioapi_0.15.0            
 [25] RSQLite_2.3.4                 generics_0.1.3               
 [27] BiocIO_1.12.0                 gtools_3.9.5                 
 [29] crosstalk_1.2.1               vroom_1.6.5                  
 [31] car_3.1-2                     zip_2.3.0                    
 [33] GO.db_3.18.0                  Matrix_1.6-4                 
 [35] fansi_1.0.6                   abind_1.4-5                  
 [37] lifecycle_1.0.4               multcomp_1.4-25              
 [39] whisker_0.4.1                 yaml_2.3.8                   
 [41] carData_3.0-5                 gplots_3.1.3                 
 [43] SparseArray_1.2.2             paletteer_1.5.0              
 [45] blob_1.2.4                    promises_1.2.1               
 [47] crayon_1.5.2                  lattice_0.22-5               
 [49] KEGGREST_1.42.0               zeallot_0.1.0                
 [51] pillar_1.9.0                  knitr_1.45                   
 [53] rjson_0.2.21                  estimability_1.4.1           
 [55] admisc_0.34                   codetools_0.2-19             
 [57] fastmatch_1.1-4               getPass_0.2-4                
 [59] vctrs_0.6.5                   png_0.1-8                    
 [61] cellranger_1.1.0              gtable_0.3.4                 
 [63] rematch2_2.1.2                datawizard_0.9.0             
 [65] cachem_1.0.8                  xfun_0.41                    
 [67] S4Arrays_1.2.0                mime_0.12                    
 [69] correlation_0.8.4             coda_0.19-4                  
 [71] survival_3.5-7                statmod_1.5.0                
 [73] TH.data_1.1-2                 interactiveDisplayBase_1.40.0
 [75] ellipsis_0.3.2                nlme_3.1-164                 
 [77] bit64_4.0.5                   progress_1.2.3               
 [79] filelock_1.0.3                rprojroot_2.0.4              
 [81] bslib_0.6.1                   KernSmooth_2.23-22           
 [83] colorspace_2.1-0              DBI_1.1.3                    
 [85] tidyselect_1.2.0              processx_3.8.3               
 [87] emmeans_1.8.9                 bit_4.0.5                    
 [89] compiler_4.3.2                curl_5.2.0                   
 [91] git2r_0.33.0                  xml2_1.3.6                   
 [93] ggdendro_0.1.23               DelayedArray_0.28.0          
 [95] plotly_4.10.3                 bayestestR_0.13.1            
 [97] rtracklayer_1.62.0            caTools_1.18.2               
 [99] callr_3.7.3                   rappdirs_0.3.3               
[101] digest_0.6.33                 rmarkdown_2.25               
[103] XVector_0.42.0                htmltools_0.5.7              
[105] pkgconfig_2.0.3               highr_0.10                   
[107] fastmap_1.1.1                 rlang_1.1.2                  
[109] htmlwidgets_1.6.4             shiny_1.8.0                  
[111] farver_2.1.1                  jquerylib_0.1.4              
[113] zoo_1.8-12                    jsonlite_1.8.8               
[115] BiocParallel_1.36.0           RCurl_1.98-1.13              
[117] GenomeInfoDbData_1.2.11       parameters_0.21.3            
[119] munsell_0.5.0                 Rcpp_1.0.11                  
[121] babelgene_22.9                visdat_0.6.0                 
[123] stringi_1.8.3                 zlibbioc_1.48.0              
[125] MASS_7.3-60                   plyr_1.8.9                   
[127] parallel_4.3.2                Biostrings_2.70.1            
[129] splines_4.3.2                 pander_0.6.5                 
[131] hms_1.1.3                     locfit_1.5-9.8               
[133] ps_1.7.5                      uuid_1.1-1                   
[135] ggsignif_0.6.4                effectsize_0.8.6             
[137] futile.options_1.0.1          BiocVersion_3.18.1           
[139] evaluate_0.23                 lambda.r_1.2.4               
[141] BiocManager_1.30.22           tzdb_0.4.0                   
[143] httpuv_1.6.13                 MatrixModels_0.5-3           
[145] BayesFactor_0.9.12-4.6        xtable_1.8-4                 
[147] restfulr_0.0.15               rstatix_0.7.2                
[149] later_1.3.2                   memoise_2.0.1                
[151] GenomicAlignments_1.38.0      timechange_0.2.0             
[153] here_1.0.1